Evolutionary Optimized 3D WiFi Antennas Manufactured via Laser Powder Bed Fusion

The swift and automated design of antennas remains a challenging aspect in research due to the specific design needs for individual applications. Alterations in resonance frequency or boundary conditions necessitate time-consuming re-designs. Though the application of evolutionary optimization and generative methods in general to antenna design has seen success, it has been mostly restricted to two-dimensional structures. In this work, we present an approach for designing three-dimensional antennas using a genetic algorithm coupled with a region-growing algorithm - to ensure manufacturability - implemented in Matlab manufactured via laser powder bed fusion (LPBF). As a simulation tool for optimization CST is used. The antenna has been optimized in a completely automated manner and was produced using the metal 3D printing technology LPBF and aluminium based AlSi10Mg powder. The presented concept, which builds upon previous two-dimensional techniques, allows for significant flexibility in design, adapting to changing boundary conditions, and avoiding the geometric restrictions seen in prior methods. The optimized antenna has a size of
$3.01 \text {cm} \times 3.43 \text {cm} \times 1.67 \text {cm}$
and was measured in an anechoic chamber. According to measurements a minimum reflection coefficient of
$\mathrm {-19.95\,\, \text {dB}}$
at
$\mathrm {2.462~ \text {G} \text { Hz} }$
and a bandwidth of
$\mathrm {308.8~ \text {M} \text { Hz} }$
are observed. CST simulation results predict an efficiency of
$\mathrm {98.91~\%}$
and the maximum antenna gain is measured at
$\mathrm {2.45~ \text {G} \text { Hz} }$
to be
$\mathrm {3.27~ \text {dB} i}$
. Simulations made with CST and Ansys HFSS and measurements are in excellent agreement with a deviation of the resonance frequency of only
$\mathrm {0.13~\%}$
, thus further establishing genetic algorithms as a highly viable option for the design of novel antenna structures.